Hierarchical discrimination of grasp modes using surface EMGs

Classification and discrimination of EMG patterns of preshaping hand motion are studied. The motions were performed by grasping six objects: small cylinder, large cylinder, small ball, large ball, small disk (precision, pinch grasp) and key (lateral grasp). An earlier work at SDSU has shown that the high successful classification rate of over 90% can be achieved. In this work the possibilities of hierarchical cluster discrimination and classification were explored. It was shown that hierarchical approach yields faster neural network training. It is also shown that the Kohonen´s self-organizing maps can be successfully used in determination of the cluster hierarchy which characterizes a particular subject